Bridged Clustering: Semi-Supervised Sparse Bridging

We introduce Bridged Clustering, a semi-supervised framework to learn predictors from any unpaired input X and output Y dataset. Our method first clusters X and Y independently, then learns a sparse, interpretable bridge between clusters using only a few paired examples. At inference, a new input x is assigned to its nearest input cluster, and the centroid of the linked output cluster is returned as the prediction haty. Unlike traditional SSL, Bridged Clustering explicitly leverages output-only data, and unlike dense transport-based methods, it maintains a sparse and interpretable alignment. Through theoretical analysis, we show that with bounded mis-clustering and mis-bridging rates, our algorithm becomes an effective and efficient predictor. Empirically, our method is competitive with SOTA methods while remaining simple, model-agnostic, and highly label-efficient in low-supervision settings.